Different model-dependent techniques have been proposed to alleviate the poor coverage of objects [1]}, [2]}, [3]}, [4]}, [5]}. In [6]}, authors utilized CAMs from multiple convolution layers with different receptive fields to enlarge the discriminative regions. [4]} combines CAMs of different classes to identify foreground and background regions in an image. [8]}, [9]} propose to suppress background regions to help to generate a foreground map with high confidence, guided by area constraints.
For a robust training, [10]} incorporates an encoder-decoder layer between the shallow layers, and a generator to mask a part of the image. Low-level feature based activation map (FAM) [11]} utilizes multiple classifiers to generate a foreground map that is decomposed into multiple part regions. This is used to train a network to produce a final semantic-agnostic map. Self-produced guidance (SPG) [12]} separates foreground and background regions for guiding shallow learning while expanding to less discriminative areas. Shallow feature-aware pseudo supervised object localization (SPOL) [13]} employs a multiplicative fusion strategy for harvesting highly confident regions, and then using those masks as pseudo-labels for training a segmentation network. Inter-image communication (I\(^2\) C) [14]} increases the robustness of localization maps by considering the correlation of similar images within a class. Structure-preserving activation (SPA) [15]} seeks to preserve the object's structure within a CAM, although this method still produces blobby boundaries. Similarly, Strengthen learning tolerance (SLT) [16]} groups images of similar classes to increase the network's tolerance, and force the localization map to expand towards the object's boundaries. Also, the full resolution CAM (F-CAM) [17]} has been proposed to expand activation maps to cover full object beyond discriminative regions. Compared to F-CAM, our proposed method is able to define sampling regions for building efficient pseudo-label with the help of classifier. This helps our model to focus only on target object instead of expanding the activation to other objects in the concerned image.
